African Nova Scotian nurses’ perceptions and experiences of leadership: a qualitative study informed by Black feminist theory
Bibliographic record
Abstract
BACKGROUND: People of African Nova Scotian (ANS) ancestry are a culturally distinct group who experience numerous socioeconomic inequities and health disparities, secondary to structural and social determinants of health. Understanding the experiences of ANS health practitioners is important in addressing anti-Black racism in health care. We sought to critically examine the leadership experiences of ANS nurses in health care practice. METHODS: We used Black feminist theory to guide this qualitative study. We conducted 1-on-1 semistructured telephone interviews with ANS nurses and analyzed interview transcripts using Critical Discourse Analysis. RESULTS: We interviewed 18 nurses of ANS ancestry. We conceptualized study findings in 3 overarching areas: People of ANS ancestry as a distinct people, institution of care, and leadership philosophy and practice. Each area, and its corresponding themes and subthemes, illustrated an emergent understanding of factors that influence leadership among ANS nurses, such as socialization, early exposure to care and diversity in health care. Participants perceived and practised leadership in a manner that transcended formal titles or designations. INTERPRETATION: African Nova Scotian ancestry is implicated in the perception and practice of leadership among ANS nurses, who considered leadership to be a fundamental component of nursing practice that was grounded in community-oriented care. This study provides new insights that could inform recruitment, retention and representation of ANS people in nursing and other health professions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".